Search Results (32)

Unexpected cut-in events may elicit driver anger even when braking is partly supported by driver-assistance systems. This preliminary simulator study examined whether SAE Level 1 longitudinal braking assistance alters affective responses to dangerous cut-in events. Ten young male licensed drivers completed three within-subject scenarios: manual driving without a cut-in, manual driving with a dangerous cut-in, and SAE Level 1 braking assistance with a dangerous cut-in. STAXI State Anger and salivary amylase were measured before and after each scenario. STAXI State Anger showed an overall scenario effect (p = 0.0045), but Holm-corrected post hoc comparisons were not statistically significant. In particular, the data did not indicate an anger-reducing effect of braking assistance compared with manual driving during the same cut-in event. Salivary amylase showed no significant scenario effect (p = 0.273). These preliminary findings suggest that physical braking assistance alone may be insufficient to mitigate anger-related responses to sudden cut-in events, and they motivate future controlled studies of cognitive support and system intent communication in ADAS contexts. Full article

As a core component of childbearing costs, women’s opportunity costs provide a crucial perspective for explaining the current decline in fertility rates. Recognizing the reciprocal causality between women’s opportunity costs and fertility decisions, this study examines their statistical correlation using micro-level data from the China Family Panel Studies (CFPS). Building on these empirical insights, we develop a household fertility decision-making model that incorporates women’s opportunity costs, calibrating the parameters through structural estimation to quantitatively explore its impact on fertility choices. The quantitative empirical findings reveal a significantly negative correlation between women’s opportunity costs and the actual number of children in a household. The theoretical analysis demonstrates that an intensifying motherhood penalty and prolonged career interruptions due to childbirth both lead to a reduction in the equilibrium number of children. Furthermore, higher educational attainment and increasing child-rearing costs exert a pronounced inhibitory effect on fertility intentions. Policy simulations further indicate that, compared to short-term or one-off incentives, continuous fertility subsidies and the implementation of free childcare policies are more effective in offsetting opportunity costs and boosting household fertility intentions. Full article

Critical Thinking (CT) is recognized as a foundational competency for professional readiness, innovation, and ethical reasoning in higher education, enabling students to analyze information, evaluate evidence, and make reasoned decisions in complex environments. The rapid integration of Generative Artificial Intelligence (GenAI) tools, such as large language models, presents new opportunities and risks for CT development. This study conducts a systematic literature review to synthesize empirical evidence on the pedagogical implications and cognitive impact of GenAI on students’ CT. Following PRISMA guidelines, and search terms around GenAI Tools, Critical Thinking And Higher Education, on five major education research databases—Web of Science; Scopus; EBSCOhost (Education Source, ERIC, and APA PsycInfo); and Compendex and Inspec (Elsevier)—63 empirical studies published between January 2023 and April 2025 were analyzed across higher education contexts, disciplines, and intervention designs. Results indicate that GenAI offers notable cognitive affordances, including scaffolding reflective reasoning, promoting self-regulation, and facilitating iterative dialogue and argument evaluation. Pedagogical strategies clustered into four primary integration typologies: AI-based feedback prompts, dialogue simulation and reflection, AI-supported peer review, and critical engagement with AI-generated content. Nearly half of the studies reported statistically significant CT improvements, particularly when GenAI use was guided by structured prompts, reflective activities, and performance-based assessment. However, multiple risks persist, including cognitive offloading, uncritical acceptance of AI outputs, and diminished intellectual autonomy, especially in unguided or surface-level usage. This review highlights the need for intentional pedagogical design, validated CT assessment tools, and longitudinal studies to ensure GenAI acts as a catalyst rather than a substitute for human reasoning. By identifying effective integration strategies and outlining potential pitfalls, this study provides evidence-informed guidance for educators and institutions aiming to responsibly leverage GenAI to strengthen students’ CT skills. Full article

To address the information-processing burden faced by consumers in green consumption markets due to complex carbon footprint labels, opaque certification standards, and vague descriptions of environmental benefits, this study proposes a generative artificial intelligence (GenAI)-based precision recommendation mechanism for green products. The mechanism aims to enhance cognitive fluency and promote low-carbon purchase decisions. An experimental system, termed Eco-GenRec, is developed by integrating large language models (LLMs), multimodal generation, and retrieval-augmented generation (RAG) techniques to enable personalized presentation of green product information. Based on inferred user cognitive styles, the system transforms product information into chart-based representations for analytical users or emotionally framed scenario narratives for intuitive users. This study is conducted on a web-based simulated shopping platform and employs a fully randomized design. A total of 1000 participants are randomly assigned to either a standardized information display group (control group) or an Eco-GenRec-generated display group (experimental group). Participants are drawn from diverse socioeconomic backgrounds and cover a wide age range. The sample exhibits substantial demographic diversity, which enhances the representativeness of the findings. Cognitive fluency and low-carbon purchase conversion rates are measured as the primary outcomes. The results show that the Eco-GenRec group achieves a significantly higher cognitive fluency score (M = 5.68, SD = 0.89) than the control group (M = 4.60, SD = 1.01). This represents an increase of 23.4% (t = 18.34, p < 0.001, effect size d = 1.17). In addition, the low-carbon purchase conversion rate in the experimental group (36.3%) is significantly higher than that in the control group (17.6%). The absolute increase of 18.7% is statistically significant (χ² = 70.28, p < 0.001, effect size Cramér’s V = 0.265). Under conditions of high cognitive-style matching, the conversion rate improvement reaches 27.2%. Mechanism analysis shows that cognitive fluency mediates the relationship between GenAI-based recommendations and purchase intention. By transforming abstract environmental parameters into intuitive and easily interpretable content, artificial intelligence reduces information-processing burden and activates positive affect and trust among consumers. Overall, this study empirically validates the effectiveness of GenAI in green product recommendation. It provides a practical pathway for addressing the “comprehension barrier” in green consumption and extends the theoretical boundaries of research on cognitive fluency and low-carbon decision-making. Full article

Control of elbow exoskeletons using muscular signals, although promising for the rehabilitation of millions of patients, has not yet been widely commercialized due to challenges in real-time intention estimation and management of dynamic uncertainties. From a practical perspective, millions of patients with stroke, spinal cord injury, or neuromuscular disorders annually require active rehabilitation, and elbow exoskeletons with precise and safe motion intention tracking capabilities can restore functional independence, reduce muscle atrophy, and lower treatment costs. In this research, an intelligent control framework was developed for an elbow joint exoskeleton, designed with the aim of precise and safe real-time tracking of the user’s motion intention. The proposed framework consists of two main stages: (a) real-time estimation of desired joint angle (as a proxy for movement intention) from High-Density Surface Electromyography (HD-sEMG) signals using an LSTM network and (b) implementation and comparison of three PID, impedance, and sliding mode controllers. A public EMG dataset including signals from 12 healthy individuals in four isometric tasks (flexion, extension, pronation, supination) and three effort levels (10, 30, 50 percent MVC) is utilized. After comprehensive preprocessing (Butterworth filter, 50 Hz notch, removal of faulty channels) and extraction of 13 time-domain features with 99 percent overlapping windows, the LSTM network with optimal architecture (128 units, Dropout, batch normalization) is trained. The model attained an RMSE of 0.630 Nm, R² of 0.965, and a Pearson correlation of 0.985 for the full dataset, indicating a 47% improvement in R² relative to traditional statistical approaches, where EMG is converted to desired angle via joint stiffness. An assessment of 12 motion–effort combinations reveals that the sliding mode controller consistently surpassed the alternatives, achieving the minimal tracking errors (average RMSE = 0.21 Nm, R² ≈ 0.96) and showing superior resilience across all tasks and effort levels. The impedance controller demonstrates superior performance in flexion/extension (average RMSE ≈ 0.22 Nm, R² > 0.94) but experiences moderate deterioration in pronation/supination under increased loads, while the classical PID controller shows significant errors (RMSE reaching 17.24 Nm, negative R² in multiple scenarios) and so it is inappropriate for direct myoelectric control. The proposed LSTM–sliding mode hybrid architecture shows exceptional accuracy, robustness, and transparency in real-time intention monitoring, demonstrating promising performance in offline simulation, with potential for real-time clinical applications pending hardware validation for advanced upper-limb exoskeletons in neurorehabilitation and assistive applications. Full article

Although the accidental or intentional explosions produced in industrial facilities or in urban areas are events with low probability, they have a high destructive potential and potential for human injuries and/or fatalities. One of the types of such events is given by detonation of improvised explosive devices (IEDs)—dirty bombs for terrorist purposes—which may produce a high number of metallic fragments. Studying mass and spatial distributions of these fragments is useful for evaluating their lethality and destructive potential and may help to implement adequate protective measures. This work brings a closer insight into the fragment dispersion around the detonation of a steel-enclosed C4 charge with cylindrical symmetry. In this respect a specific approach involving both detonation experiments and numerical simulations performed by home-made and commercial software packages for investigation of the fragmentation process and accompanying angular scattering of the fragments was proposed. Special algorithms, which allow the estimation of the spatial distributions of fragments from the numerical analysis of perforations made by the metallic fragments generated by such IEDs on surrounding material walls, are developed. Further, numerical simulations of a similar IED device provided output parameters related to the statistical distributions of mass, kinetic energy and position of the fragments. Experimental fragmentation generated a recovered mass distribution (94 fragments of 67.5 g) that was compared with that extracted from simulation, revealing a reasonable agreement on the 0.3–1 g range. In the case of simulations, 300 fragments from a total number of 374 showed a mass ranging from 0.004 to 0.3 g. The simulations showed that the middle part of the steel case generated fragments of kinetic energy over 4 kJ and its ends generated fragments of kinetic energy under 1 kJ. Experimental fragment scattering distributions were investigated with specific home-made numerical algorithms, which, based on a set of images, analysed the correlations between spatial coordinates of perforations made by fragments on surrounding special panels and provided histograms that are discussed in relation with the fragment-induced lethality degree. Full article

Recent technological advancements have resulted in the emergence of portable electronic devices (PEDs), including mobile phones equipped with satellite communication capabilities. These devices generally emit higher power, which can potentially cause electromagnetic interference to GPS antennas. This study uses both simulation and experimental methods to evaluate the interference path loss (IPL) between PEDs located inside an A320 aircraft and an external GPS antenna. The effects of PED location, antenna polarization, and frequency bands on IPL were simulated and analyzed. Additionally, measurement experiments were conducted on an A320 aircraft, and statistical methods were used to compare the experimental data with the simulation results. Considering the front-door coupling of both spurious and intentional radiated emissions, the measured IPL is up to 15 ± 3 dB lower than the $IP{L}_{target}$. This result should be interpreted with caution. This issue offers new insights into the potential risks of electromagnetic interference in aviation environments. The findings help quantify the probability of interference with GPS antennas. Furthermore, the modeling simplification method used in this study may be applicable to the analysis of other large and complex structures. Full article

Background/Objectives: Mechanical ventilation management is a critical competency for intensive care unit (ICU) nurses; however, traditional training methods are often insufficient to prepare nurses for the complexities of alarm management and clinical decision-making. This study aimed to evaluate the effectiveness of a 360-degree virtual reality (VR)-based mechanical ventilation nursing education program for ICU nurses in Korea. Methods: A quasi-experimental pre-test–post-test design was employed with 65 ICU nurses (32 in the experimental group and 33 in the control group). Data were collected from May to October 2023. The VR-based program, developed using the ADDIE instructional design model, incorporated simulation-based scenarios focusing on ventilator alarm management and clinical reasoning. Outcome measures included knowledge of ventilation nursing, self-efficacy, clinical reasoning, learning immersion, turnover intention, and educational satisfaction. Data were analyzed using normality tests, descriptive statistics, independent t-tests, and paired t-tests. Results: The experimental group demonstrated significantly greater improvements in knowledge (Δ = 5.54), self-efficacy (Δ = 0.94), clinical reasoning (Δ = 0.76), and learning immersion (Δ = 0.88) compared to the control group (all p < 0.001), where Δ denotes the change score (post-test minus pre-test). Post-test assessments were conducted immediately after the intervention. Educational satisfaction was also significantly higher in the experimental group (p < 0.001). No significant difference was observed in turnover intention between the groups, suggesting a limited short-term impact on this outcome. Conclusions: A 360-degree VR-based education program effectively enhanced key competencies among ICU nurses. While these findings reflect short-term outcomes, future research is warranted to assess the long-term effects and sustainability of VR-based learning in ICU continuing education. Full article

We propose Observer-Linked Branching (OLB), a mathematically rigorous extension of quantum theory in which an observer’s cognitive commitment actively modulates collapse dynamics at macroscopic scales. The OLB framework rests on four axioms, employing a norm-preserving nonlinear Schrödinger evolution and Lüders-type projection triggered by crossing a cognitive commitment threshold. Our expanded formalism provides five main contributions: (1) deriving Lie symmetries of the observer–environment interaction Hamiltonian; (2) embedding OLB into the Consistent Histories and path-integral formalisms; (3) multi-agent network simulations demonstrating intentional synchronisation toward shared macroscopic outcomes; (4) detailed statistical power analyses predicting measurable biases (up to ~5%) in practical experiments involving traffic delays, quantum random number generators, and financial market sentiment; and (5) examining the conceptual, ethical, and neuromorphic implications of intent-driven reality selection. Full reproducibility is ensured via the provided code notebooks and raw data tables in the appendices. While the theoretical predictions are precisely formulated, empirical validation is ongoing, and no definitive field results are claimed at this stage. OLB thus offers a rigorous, norm-preserving and falsifiable framework to empirically test whether cognitive engagement modulates macroscopic quantum outcomes in ways consistent with—but extending—standard quantum predictions. Full article

Aim: An Early-Stage Non-Small Cell Lung Cancer (ES-NSCLC) patient candidate for stereotactic body radiotherapy (SBRT) may start their treatment without a histopathological assessment, due to relevant comorbidities. The aim of this study is twofold: (i) build prognostic models to test the association between CT-derived radiomic features (RFs) and the outcomes of interest (overall survival (OS), progression-free survival (PFS) and loco-regional progression-free survival (LRPFS)); (ii) quantify whether the combination of clinical and radiomic descriptors yields better prediction than clinical descriptors alone in prognostic modeling for ES-NSCLC patients treated with SBRT. Methods: Simulation CT scans of ES-NSCLC patients treated with curative-intent SBRT at the European Institute of Oncology (IEO), Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Milan, Italy between 2013 and 2023 were retrospectively retrieved. PyRadiomics v3.0.1 was used for image preprocessing and subsequent RFs extraction and selection. A radiomic score was calculated for each patient, and three prognostic models (clinical model, radiomic model, clinical-radiomic model) for each survival endpoint were built. Relative performances were compared using the C-index. All analyses were considered statistically significant if p < 0.05. The statistical analyses were performed using R Software version 4.1. Results: A total of 100 patients met the inclusion criteria. Median age at diagnosis was 76 (IQR: 70–82) years, with a median Charlson Comorbidity Index (CCI) of 7 (IQR: 6–8). At the last available follow-up, 76 patients were free of disease, 17 were alive with disease, and 7 were deceased. Considering relapses, progression of any kind was diagnosed in 31 cases. Regarding model performances, the radiomic score allowed for excellent prognostic discrimination for all the considered endpoints. Of note, the use of RFs alone proved to be more informative than clinical characteristics alone for the prediction of both OS and LRPFS, but not for PFS, for which the individual predictive performances slightly favored the clinical model. Conclusion: The use of RFs for outcome prediction in this clinical setting is promising, and results seem to be rather consistent across studies, despite some methodological differences that should be acknowledged. Further studies are being planned in our group to externally validate these findings, and to better determine the potential of RFs as non-invasive and reproducible biomarkers in ES-NSCLC. Full article

As China’s urbanization progresses, the urban heat island (UHI) effect has become more pronounced, impacting the health of residents and the activity intentions of visitors within historic urban areas. This study focuses on the historic district of the Three Lanes and Seven Alleys Tourist Area (SFQX) in Fuzhou, where simulations were conducted on four representative streets across various times during a typical summer meteorological day. Typological methods were employed to simplify neighborhood modeling, and Phoenics software was utilized to simulate the neighborhood’s wind environment and the outdoor pedestrian thermal comfort index. Aspect ratio (AR), sky view factor (SVF), air velocity (Va), and universal thermal climate index (UTCI) values at specific locations were collected for statistical analysis. The findings reveal that: (1) the N–S orientation exhibits more significant correlations between Va, the UTCI, and street geometry compared to the E–W orientation; (2) the relationship between SVF and the UTCI fluctuates with time; (3) areas with higher AR values, such as medium and deep canyons, offer better thermal comfort for outdoor pedestrians; and (4) at 8:00, the UTCI and wind speed show minimal correlations with street geometry and direction, being predominantly influenced by objective climatic factors. These insights are expected to significantly inform the geometric design and planning of streets in Fuzhou’s historic districts, aiming to create more comfortable outdoor environments for inhabitants and visitors alike. Full article

Room reverberation can affect oral/aural communication and is especially critical in computer analysis of voice. High levels of reverberation can distort voice recordings, impacting the accuracy of quantifying voice production quality and vocal health evaluations. This study quantifies the impact of additive simulated reverberation on otherwise clean voice recordings as reflected in voice metrics commonly used for voice quality evaluation. From a larger database of voice recordings collected in a low-noise, low-reverberation environment, voice samples of a sustained [a:] vowel produced at two different speaker intents (comfortable and clear) by five healthy voice college-age female native English speakers were used. Using the reverb effect in Audacity, eight reverberation situations indicating a range of reverberation times (T20 between 0.004 and 1.82 s) were simulated and convolved with the original recordings. All voice samples, both original and reverberation-affected, were analyzed using freely available PRAAT software (version 6.0.13) to calculate five common voice parameters: jitter, shimmer, harmonic-to-noise ratio (HNR), alpha ratio, and smoothed cepstral peak prominence (CPPs). Statistical analyses assessed the sensitivity and variations in voice metrics to a range of simulated room reverberation conditions. Results showed that jitter, HNR, and alpha ratio were stable at simulated reverberation times below T20 of 1 s, with HNR and jitter more stable in the clear vocal style. Shimmer was highly sensitive even at T20 of 0.53 s, which would reflect a common room, while CPPs remained stable across all simulated reverberation conditions. Understanding the sensitivity and stability of these voice metrics to a range of room acoustics effects allows for targeted use of certain metrics even in less controlled environments, enabling selective application of stable measures like CPPs and cautious interpretation of shimmer, ensuring more reliable and accurate voice assessments. Full article

Recently, with climate deterioration and environmental pollution, consumers are becoming more and more aware of the use of sustainable energy. In particular, the demand for electric vehicles that use sustainable energy is also increasing. In addition, due to the simple driving principle of pure electric vehicles, many electric vehicles developed by electronics companies are continuously being launched. Electric vehicles not only use renewable energy to protect the environment but also save on various usage expenses, so they are expected to become the main products in the mobile travel equipment market in the future. This study aims to explore the impact of product design dimensions on electric vehicle (EV) purchase intentions, provide a theoretical basis for companies’ differentiation strategies, and reflect the impact of product design on purchase intention. This study uses Davis’s TAM combined with environmental awareness (EA) for analysis; an online survey was conducted on Chinese (n = 468) and Korean (n = 409) consumers, both male and female, aged 20–60 years and above. We found that, for Chinese consumers, the aesthetic and symbolic dimensions do not affect perceived usefulness and perceived ease of use, but they do affect environmental awareness, while the functional dimension affects not only perceived ease of use and usefulness but also environmental awareness. For Korean consumers, the aesthetic, functional, and symbolic dimensions all affect perceived ease of use and environmental awareness, but perceived usefulness is only affected by aesthetics and environmental awareness. Through simulation analysis, the results show that perceived ease of use, usefulness, and environmental awareness all directly affect purchase intentions. Perceived ease of use and environmental awareness are particularly important for Chinese consumers, while Korean consumers pay more attention to the test drive experience and environmental awareness. The results show that electric vehicle manufacturers should develop new technologies for the Chinese market to attract consumers, while in the Korean market, they should improve perceived usefulness through test drives and pay attention to environmental awareness. Specific statistical data show that both Chinese and Korean consumers assign importance to the impact of environmental awareness on purchase intention, proving the importance of environmental awareness. The results of this study will be of great reference value to electric vehicle manufacturers, policymakers, and consumer behavior researchers, helping them to better understand the role of product design in improving the market acceptance of electric vehicles. Full article

Lane changing behavior (LCB) prediction is a crucial functionality of advanced driver-assistance systems and autonomous vehicles. Predicting whether or not the driver of a considered ego vehicle is likely to change lanes in the near future plays an important role in improving road safety and traffic efficiency. Understanding the underlying intentions behind the driver’s behavior is an important factor for the effectiveness of assistance and monitoring systems. Machine learning (ML) algorithms have been broadly used to predict this behavior by analyzing datasets of traffic and driving data related to the considered ego vehicle. However, this technology has not yet been widely adopted in commercial products. Further improvements in these algorithms are necessary to enhance their robustness and reliability. In some domains, receiver operating characteristic and precision-recall curves are commonly used to evaluate ML algorithms, not considering the effects of process parameters in the evaluation, while it might be necessary to access the performance of these algorithms with respect to such parameters. This paper proposes the use of deep autoencoders to extract multi-level features from datasets, which can then be used to train an ensemble of classifiers. This allows for taking advantage of high feature-extraction capabilities of deep learning models and improving the final result using ensemble learning techniques. The concept of probability of detection is used in combination with the networks employed here to evaluate which classifiers can detect the correct LCB better in a statistical sense. Applications on data acquired from a driving simulator show that the proposed method can be adopted to improve the reliability of the classifiers, and ensemble ANNs perform best in predicting the upcoming human behavior in this dynamical context earlier than 3 s before the event itself. Full article

Border control systems at Europe’s Schengen (and worldwide) borders are necessary to mitigate cross-border threats, but are perceived as free-traveling bottlenecks. Today’s applicable European regulations demand rule-based control schemes and do not allow risk-based elements. A policy shift towards risk-based border control has been considered in several studies and research (including HEU projects). However, there is a lack of scientific evidence on how they compare with existing rule-based schemes. This paper aims to fill that gap. The simulation allows design of a realistic border control system. The passenger flow is modeled via travelers with good and bad intents. The border control system includes decision-making elements to classify travelers into risk groups. System elements including operators and their interaction were modeled in terms of statistical distributions based on the subject matter experts’ input. The performance is estimated across security effectiveness, resource usage, passenger flow, and traveler experience. Assessment of a set of simulations reveals better scalability of risk-based systems in terms of resource usage and passenger flow. The potential factors to improve the detection rate of the border control process are also studied. Despite having several benefits, the model demonstrates that social acceptance of the risk-based system is the limiting factor for increased scalability. Full article